68 results about "Sentence segmentation" patented technology

The invention provides a tunnel risk reasoning method fusing a knowledge graph and a large language model, which comprises the following steps of: obtaining structured monitoring data and unstructured text data, adopting methods such as field standardization for the structured monitoring data to realize a unified format, adopting methods such as sentence segmentation and word segmentation for the unstructured text data to realize the unified format, and obtaining the structured monitoring data and the unstructured text data; the method comprises the following steps of: extracting entities from data by utilizing a model, extracting a relationship between the entities based on the entities, forming basic triads, forming a sub-graph by the basic triads, integrating to form a knowledge graph, generating a natural language, extracting the sub-graph related to the natural language from the knowledge graph, and converting the sub-graph into a sub-graph in a vector form by utilizing a graph embedding algorithm. The entities and the relation paths of the entities serve as explicit reasoning clues, the natural language, the sub-maps in the vector form and the explicit reasoning clues are input into a large language model, natural language output is generated, multi-source data information is integrated, and high-precision and interpretable tunnel risk early warning is output through the large language model.

A public opinion analysis visualization platform construction method based on sentiment analysis belongs to the technical field of natural language processing, and specifically comprises the following steps: 1, collecting related corpora and data required by sentiment analysis; 2, preprocessing comment data required by sentiment analysis; and filtering invalid texts, and performing sentence segmentation processing. 3, firstly performing text classification on the processed data, then performing sentiment analysis, judging sentiment tendency in a comment text, and finally performing visual display; and 4, performing public opinion early warning based on an obtained sentiment analysis result and a popularity data analysis result. And 5, generating an independent public opinion evaluation report and a summary report through a related API interface of the local large language model. Through the method, the public opinion response efficiency can be remarkably improved, the risk identification capability is enhanced, the complex language understanding bottleneck is broken through, and meanwhile the manual analysis cost is reduced.

The invention provides a false news detection method and device and terminal equipment, and is suitable for the technical field of data processing.The method comprises the steps that statement segmentation and invalid statement filtering processing are conducted on news text information to be detected, and multiple pieces of news statement information to be detected are obtained; generating multiple pieces of to-be-detected news text structured information according to the to-be-detected news text information, the multiple pieces of to-be-detected news statement information, preset to-be-detected news text extraction guide information and a preset to-be-detected news text structured information extraction model; and generating multiple pieces of news text detection information according to the to-be-detected news text information, the multiple pieces of to-be-detected news text structured information and a preset news text detection model. According to the method, the requirements of scenes such as manual review and judicial evidence collection on interpretability and traceability are met, and the accuracy, reliability and engineering reproducibility of false news detection in a complex context are remarkably improved.

The invention discloses an ancient Chinese automatic sentence segmentation and semantic analysis system based on deep learning, and relates to the technical field of ancient Chinese analysis, according to the ancient Chinese automatic sentence segmentation and semantic analysis system based on deep learning, a sentence segmentation module is fused with a gated loop unit (GRU) and a convolutional neural network (CNN), and image font features are combined, so that a sentence segmentation result is obtained; the problems of semantic ambiguity, long-distance dependence, common and false characters and abnormal characters can be effectively solved; a knowledge graph containing historical and cultural common knowledge and vocabulary dynamic relations is constructed through a semantic analysis module, prior knowledge is injected through a graph neural network, and the semantic understanding depth is improved; a sentence segmentation result containing a sentence segmentation basis and a visual semantic analysis result are provided through an output module, so that a user can clearly master processing logic, and credibility and convenience are improved; a large number of unlabeled ancient Chinese texts are efficiently processed by the system, and values of various cultural heritage such as classics and inscriptions are fully mined; the development of grammar and vocabulary research is promoted, and ancient Chinese culture inheritance and propagation are assisted.

This application discloses an intelligent method and apparatus for detecting active speech with sentence segmentation based on multi-state temporal modeling. The method includes: receiving audio signals from at least one channel; extracting acoustic feature sequences from the audio signals using a target speech recognition model corresponding to the number of channels; determining the probability distribution of each speech frame corresponding to the acoustic feature sequences belonging to different speech activity states, obtaining a state sequence corresponding to each channel, wherein the speech activity state includes at least one of the following: initial silence state, speech state, intra-turn pause silence state, and inter-turn sentence segmentation silence state; and determining the time of sentence segmentation in the audio signal based on the state sequence. This application solves the technical problem of erroneous sentence segmentation in speech activity detection based on a fixed silence threshold in related technologies.

The invention provides an intelligent processing method and system from structured data to a text based on a natural language, and relates to the technical field of data processing.The method comprises the steps that the text chunk analysis process is optimized and adjusted according to analysis optimization parameters, sentences are segmented into non-overlapping phrases with syntactic function labels, and the text after chunk analysis is obtained; performing syntactic and semantic structure analysis on the text subjected to block analysis, and establishing a semantic association relationship between internal structures of sentences through component analysis, dependency analysis and semantic dependency graph analysis to obtain structured semantic information; performing multi-sentence logic association analysis on the structured semantic information on a chapter level to obtain semantic information of the whole chapter; and based on the semantic information of the overall chapter, obtaining a target natural language text by utilizing a pre-trained large language model. According to the method, the accuracy and fluency of conversion from the structured data to the text are improved.

The invention discloses an intelligent voice sentence segmentation method and system based on multi-modal fusion. The method comprises the following steps: acquiring voice data to be processed; processing the voice data by using a pre-trained acoustic sentence segmentation model to obtain an acoustic time sequence feature vector; processing the voice data by using a pre-trained text semantic model to obtain a semantic feature vector; and performing cross attention fusion on the acoustic time sequence feature vector and the semantic feature vector to obtain a fusion result, and determining a sentence segmentation result corresponding to the voice data according to the fusion result. According to the method and the device, the technical problem that the sentence segmentation position cannot be accurately determined due to the fact that acoustic features and semantic information are not fully combined in a related sentence segmentation method is solved.

The invention provides a text information recognition method and device, equipment and a storage medium. In some embodiments of the present disclosure, a first paragraph text and a second paragraph text of an audit report text are obtained; performing sentence segmentation processing on the first paragraph text and the second paragraph text to obtain a first sentence of the first paragraph text and a second sentence of the second paragraph text; combining the first clause and the second clause pairwise to obtain a sentence pair; encoding each sentence pair to obtain a sentence vector corresponding to each sentence pair; performing semantic emotion recognition on the sentence vector corresponding to each sentence pair to obtain a consistency result of each sentence pair; determining consistency information of the first paragraph text and the second paragraph text according to a consistency result of each sentence pair; based on semantic emotion recognition in the field of natural language processing, the audit report text is subjected to consistency auditing automatically, the labor cost is reduced, the auditing efficiency is improved, and the auditing accuracy is improved.

The application provides a role-aware chapter theme event argument extraction method and device, and the method comprises the following steps: obtaining argument role information of a chapter theme event of an event type according to the event type; performing sentence segmentation and title extraction on a target article to obtain a sentence set and an event title; the argument role information, the event type, and the event title constitute event-related information; constructing an argument role-aware graph by using the event-related information and the sentence set, performing event-related sentence detection, and obtaining a chapter theme event-related sentence set; taking the chapter theme event-related sentence set as input, constructing a question for each argument role, predicting all candidate arguments in the chapter theme event-related sentence set, and screening out a target argument from the candidate arguments. The method improves the model effect while maintaining the flexibility of the model.

The application discloses a multi-intention sentence segmentation method and device, a storage medium and an electronic device, relates to the technical field of smart homes, and comprises the following steps: extracting the character features of the characters included in a to-be-segmented sentence carrying multiple execution intentions; converting the to-be-segmented sentence into a feature image according to the character features, wherein the feature image is used to represent the character features through image pixels; performing semantic segmentation on the feature image to generate multiple semantic images, wherein each semantic image is used to represent a target execution intention in the multiple execution intentions; and identifying the target sub-sentence corresponding to each semantic image in the multiple semantic images to obtain multiple target sub-sentences corresponding to the to-be-segmented sentence. By adopting the technical scheme, the problems, such as low segmentation efficiency, in the process of segmenting multi-intention sentences in the related art are solved.

To solve the problem that it takes time to analyze contribution information and it is difficult to perform effective analysis.SOLUTION: An information processing system of the present disclosure includes acquisition means for acquiring post information posted for a predetermined store, classification means for acquiring a classification result obtained by classifying each divided sentence obtained by dividing a sentence included in the post information into a plurality of preset items according to content of the divided sentence, and output means for outputting aggregation information based on the number of classified divided sentences for each item based on the classification result, and outputting the divided sentence corresponding to the aggregation information. The information processing system according to (1).SELECTED DRAWING: Figure 2

The present disclosure relates to a method, device, storage medium and electronic device for sentence segmentation. The method comprises: obtaining target audio data; extracting speech recognition text corresponding to the target audio data and a first time period corresponding to each recognized character in the speech recognition text in the target audio data; performing speaker segmentation on the target audio data to obtain a second time period corresponding to each speech segment in the target audio data; and performing speaker segmentation on the speech recognition text according to the first time period corresponding to each recognized character and the second time period corresponding to each speech segment to obtain a sentence segmentation result. Thus, the speaker time period information and the time period corresponding to each character in the speech recognition text in the target audio data can be effectively utilized to perform speaker segmentation on the speech recognition text, to reasonably and effectively segment the speaker conversion, to avoid a single sentence segment containing speech content of multiple speakers, and to improve the sentence segmentation effect.

The invention relates to the technical field of tourism product analysis, in particular to an elderly tourism product and experience behavior analysis management system based on big data. The user terminal obtains feedback content which is input by a user and aims at the tourism product, and then sends the feedback content to the server; the feedback content is processed to obtain text sentences corresponding to the feedback content, and the text sentences are tourism product judgment character evaluation content of the user and are subjected to sentence segmentation processing; the server carries out word recognition on the text sentences on the basis of a tourism item word bank and an emotion judgment word bank and generates an emotion score value corresponding to the feedback content, the emotion score value can reflect the evaluation degree of the user on the tourism product, the feedback content is displayed through the management terminal, and the user experience is improved. And according to the corresponding product numbers and the corresponding emotion score values, management personnel can perform targeted analysis and optimization on the tourism products according to the feedback content and the corresponding emotion score values.

The application provides a text recognition method and device, a storage medium and a vehicle. The method comprises the following steps: determining a first probability of a character in a to-be-recognized text in a preset annotation type according to a preset NLP model; determining a second probability of the character in the to-be-recognized text in the preset annotation type according to a preset CV network model and the first probability; outputting target annotation information according to the first probability and the second probability; and performing sentence segmentation recognition on the to-be-recognized text according to the target annotation information. The application determines the target annotation information of the to-be-recognized text to perform sentence segmentation on the to-be-recognized text, thereby improving the accuracy of sentence segmentation of the to-be-recognized text, improving the accuracy of text recognition, and facilitating accurate voice control according to the text recognition result.

The application relates to the technical field of natural language processing, in particular to an AI generated text detection method and system based on a deep learning model. The method comprises the following steps: performing character normalization and sentence segmentation on a to-be-detected text, segmenting the text by a sliding window to generate a segmented sequence; constructing deep semantic discrimination features, logarithmic probability difference trace features, style structure features and entity consistency features; inputting a gate fusion discrimination network, outputting a generated probability and confidence degree after probability calibration, and outputting a segmented contribution degree; performing threshold adaptive determination according to a risk level, a text length and the confidence degree, outputting a review state and generating a traceable record when the confidence degree is insufficient; and triggering incremental updating based on drift monitoring and a feedback sample pool. The application improves cross-domain robustness and interpretability, reduces false positives and false negatives, and supports long-term stable operation.

The invention discloses a multi-subject semantic attribute binding image generation method based on a diffusion model. The method comprises the steps of improving independent coding of a single sentence into segmentation of the sentence into a plurality of sub-sentences and independent coding of the sub-sentences; improving a denoising process and improving a cross attention mechanism; clear semantic representation is obtained by independently coding clauses, a denoising process is divided into a reconstruction branch and a generation branch, intermediate potential representation is spliced and fused to improve consistency and visual quality, and meanwhile, independent attention guidance is realized by combining an improved cross attention mechanism and mask division, so that feature aliasing is avoided, and the accuracy and the reliability of the system are improved. And the local precision and the space decoupling capability are enhanced. According to the multi-entity semantic modeling and image-text alignment method, a finer and more stable solution is provided while high efficiency and universality are ensured, and the potential in multi-entity semantic modeling and image-text alignment is shown.

A kind of method for image retrieval based on NLP complex sentence segmentation, comprising: 1) complex sentence is segmented;2) multiple simple sentences are sorted;3) abstract sentence for retrieval image;4) query pruning;5) retrieve image;6) evaluate results: the performance of the method is evaluated using accuracy (Accuracy), precision (Precision), recall (Recall) and F1_score.The present application combines NLP with image retrieval, makes full use of the advantages of NLP, carries out detailed component syntax analysis and dependency syntax analysis on the complex sentence proposed by the user, inputs the sentence processed by analysis into the database as query sentence to retrieve image, not only greatly improves the effect of image retrieval, but also expands the application range of NLP.

The application relates to the technical field of natural language processing, and discloses a Chinese numeral quantifier correction method and system, computer equipment and a storage medium. The application constructs a dynamically expandable collocation word table and a numeral dictionary; performs sentence segmentation, word segmentation and part-of-speech tagging on input text, and identifies a three-element structure composed of numerals, quantifiers and nouns; excludes misjudgments on inherent expressions through a fixed phrase filtering mechanism; performs legality verification on the structure based on the collocation word table, and generates processing information for semantic correction if the verification fails; when collocation abnormalities are confirmed and the dictionary is insufficient, a large language model is triggered to replace the quantifier and adapt to the context based on the processing information, so that a final corrected sentence is generated. Through multi-layer cooperation of rule matching, word table verification and large model verification, the application effectively reduces the mis-correction rate while ensuring high-precision correction, and improves the accuracy and practicality of automatic correction of Chinese quantifiers.

The application provides a text summary extraction method based on joint training and a corresponding device, which are used to improve the problem of insufficient semantic correctness of the extracted summary text. The method comprises the following steps: obtaining a to-be-processed text, and performing sentence segmentation on the to-be-processed text to obtain a plurality of to-be-processed sentences; using a vector extraction layer in a summary extraction model to perform vectorization representation on the plurality of to-be-processed sentences to obtain word vectors and sentence vectors corresponding to the plurality of to-be-processed sentences; using a feature extraction layer in the summary extraction model to perform feature extraction on the word vectors and the sentence vectors corresponding to the plurality of to-be-processed sentences to obtain core feature vectors and similar feature vectors; and using a sentence extraction layer in the summary extraction model to extract the plurality of to-be-processed sentences according to the core feature vectors and the similar feature vectors to obtain a summary text corresponding to the to-be-processed text.

The application provides a natural language-based intelligent processing method and system for structured data to text, and relates to the technical field of data processing.The method comprises the following steps: optimizing and adjusting a text chunk analysis process according to analysis optimization parameters, segmenting a sentence into non-overlapping phrases with syntactic role labels to obtain text after chunk analysis; performing syntactic and semantic structure analysis on the text after chunk analysis, establishing semantic correlation between internal structures of the sentence through component analysis, dependency analysis and semantic dependency graph analysis, and obtaining structured semantic information; performing multi-sentence logical correlation analysis on the structured semantic information at the chapter level to obtain semantic information of the overall chapter; and obtaining a target natural language text based on the semantic information of the overall chapter by using a pre-trained large language model.The application improves the accuracy and fluency of the conversion of structured data to text.

The invention discloses a traditional Chinese medicine syndrome differentiation language model training and reasoning method based on natural language processing, and particularly relates to the technical field of medical information processing. The method comprises the following steps: performing sentence segmentation and symptom statement segmentation on a traditional Chinese medicine medical record text of a patient to construct a symptom statement sequence data set, and analyzing a co-occurrence relation and occurrence position distribution of symptom statements in a patient narrative context to generate context association features of the symptom statements; identifying functional role differences of the same symptom in different narrative contexts according to the context association features of the symptom statements to generate symptom context role annotation data; and dynamically redistributing the participation degree of the symptom characteristics in different symptom type reasoning processes based on symptom context role labeling data, carrying out constraint updating on the traditional Chinese medicine syndrome differentiation language model, and inputting a traditional Chinese medicine medical record text of a to-be-analyzed patient into the trained traditional Chinese medicine syndrome differentiation language model in a reasoning stage. And outputting a corresponding traditional Chinese medicine syndrome type judgment result.

Embodiments of the present disclosure provide a video data processing method and device, relating to the technical field of computer, which solves the problem of high error rate caused by current manual acceptance of video. The method comprises: obtaining video data to be put, wherein the video data comprises oral broadcast data and picture data; performing segmentation on the oral broadcast data through an asr interface and an open-source punctuation sentence segmentation algorithm to obtain segmented asr text; filtering invalid information in the picture data through an ocr interface and preset invalid information to obtain filtered ocr text; and obtaining a matching result of the video data according to the segmented asr text, the filtered ocr text and a preset word matching rule. The embodiments of the present disclosure are suitable for the acceptance process of brand parties for the video to be put.

The invention discloses a video conference shared content authenticity detection method based on an AI large model, and the method comprises the following steps: S1, employing a WebRTC protocol to transmit audio data through audio streams of teaching or training explanation captured by a video conference system in real time; s2, converting the unstructured audio data into a structured text which retains original language features and time sequence information; s3, cleaning the transwritten text to remove metadata, performing intelligent sentence segmentation based on semantics, and unifying a text coding format; s4, guiding an AI large model based on customized special cue words, extracting target text features from multiple dimensions, and carrying out analysis; step S5, adopting a weighted multi-feature fusion model to distribute corresponding weights for feature similarities of all dimensions and calculate a total similarity; and S6, generating a structured report containing the similarity percentage, the judgment basis and the abnormal paragraph, and returning a detection result to the video conference system.

The invention discloses a video and copywriting intelligent cooperative processing method based on deep learning, and the method comprises the following steps: carrying out the preprocessing of video data, and inputting a time sequence embedded sequence into a TimeSform model; performing sentence segmentation and word segmentation processing on the copywriting data, and inputting a fixed-length word segmentation sequence into the improved SigLIP model; performing semantic difference calculation on adjacent frame pairs to obtain a stable video time sequence feature sequence; determining a semantic change boundary according to the stable video time sequence feature sequence, and generating a video semantic representation set; respectively inputting the sentence semantic vector set and the video semantic representation set into a text coding tower and a visual coding tower of an improved SigLIP model; and carrying out standardization and matching calculation on the similarity matrix, and outputting a matching result. The method is suitable for various multi-mode content processing scenes such as short video content understanding, video abstract generation and intelligent auditing.

The invention relates to the technical field of natural language processing, in particular to an AI generated character detection method and system based on a deep learning model. The method comprises the following steps: performing character standardization, sentence segmentation and sliding window segmentation on a to-be-detected text to generate a segmentation sequence; constructing a deep semantic discrimination feature, a logarithmic probability difference generation trace feature, a style structure feature and an entity consistency feature; inputting a gating fusion discrimination network, outputting a generation probability and a confidence coefficient which are subjected to probability calibration, and outputting a segmentation contribution degree; executing threshold adaptive judgment according to the risk level, the text length and the confidence coefficient, and when the confidence coefficient is insufficient, outputting a to-be-rechecked state and generating a traceable record; and triggering incremental updating based on drift monitoring and the feedback sample pool. According to the method, the cross-domain robustness and interpretability are improved, false alarms and missing alarms are reduced, and long-term stable operation is supported.

The application discloses a knowledge graph node quantity determination method and device, electronic equipment and a storage medium, comprising: first acquiring high-frequency words of a to-be-processed text, and performing sentence segmentation on the text to obtain a plurality of sentences; determining the index serial number of the high-frequency words in each sentence, calculating the score of the sentence according to the index serial number, and screening key sentences from the plurality of sentences according to the score of the sentence, and determining the node quantity of the knowledge graph of the key sentences according to the preset total node quantity and the score of the key sentences. The score of each sentence is calculated according to the index serial number of the high-frequency words in the sentence, and the key sentences are screened according to the score, and the knowledge graph is constructed only for the key sentences, so that the focus is avoided to be lost due to the construction of the knowledge graph for each sentence; the index serial numbers of the high-frequency words in the key sentences are relatively dense, and the node quantity of each key sentence is also determined according to the score of the key sentence, so that the focus of the knowledge graph of each sentence can be highlighted.

The embodiment of the invention provides a question and answer processing method and device, a storage medium and a program product. In the question and answer processing method, if a first query message input by a user meets a context dependence condition, a response to the first query message is delayed. And if a second query message input by the user in a second input round is received, calling the target model, generating an aggregation reply result at least according to the first query message and the second query message, and outputting the aggregation reply result. Based on the implementation mode, when the first query message meets the context dependence condition, the first query message and the subsequent second query message can be subjected to aggregation response, so that the dialogue appeal information of the user can be accurately identified by aggregating the plurality of query messages input by the user in scenes such as sentence segmentation input of the user, and the user experience is improved. Therefore, the output aggregation reply result is more in line with the intention of the user, unified reply to the same topic can be realized, reply fragmentation caused by broken sentence pattern input is improved, and dialogue quality is improved.

The application provides a machine-generated text detection method, system, device and medium, and belongs to the field of natural language processing. The method comprises the following steps: performing sentence segmentation on an original text; generating a perturbed text through a four-layer structure perception perturbator, wherein the perturbator dynamically selects a perturbation type and intensity through a learnable strategy; reorganizing the text through a pre-trained encoding-decoding model and extracting a feature vector; fusing the multi-source semantic and structural features of the original text, the perturbed text and the reorganized text; and inputting a classifier to output a text detection result in combination with a comprehensive loss function. The problem of low detection accuracy of the text after machine polishing is solved.

This invention proposes a method, system, and electronic device for creating RPA processes based on natural language processing technology, belonging to the technical field of human-computer interaction. The process of creating the process includes: receiving process information in a designer and performing sentence-by-sentence matching with data in a knowledge base; when the matching result is successful, generating corresponding XML process data; when the matching result is unsuccessful, performing sentence segmentation, part-of-speech tagging, and entity recognition to generate word vectors, and performing vector matching on the word vectors to obtain XML structure data; integrating the XML structure data to obtain XML process data; parsing the XML process data to obtain the corresponding RPA process and testing it; modifying the process parameters based on the test results to obtain the final process, and synchronously updating the data in the knowledge base. This invention solves the problem of new users finding it difficult to create processes and also reduces the time users spend creating processes.

The application discloses a sensitive data identification method and device, electronic equipment, medium and program product, and relates to the technical field of data security. The sensitive data identification method comprises the following steps: performing sentence segmentation on to-be-identified text data to obtain a plurality of short sentences; counting specific words in each short sentence which are not in a preset common word library, and recording the word frequency in a corresponding time residence matrix; performing word segmentation on each short sentence according to a preset word segmentation rule, determining a plurality of word segmentation paths corresponding to each short sentence; calculating the joint probability corresponding to each word segmentation path based on the word frequency of each time residence matrix, taking the word segmentation path with the highest joint probability as a target word segmentation path, and performing sensitive word retrieval on each word segmentation to obtain a sensitive data identification result. The technical scheme of the application solves the problem that the traditional sensitive word matching algorithm is prone to errors when processing Chinese text, thereby affecting the accuracy of sensitive data identification.